Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.

MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.

Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.

Nobel laureate Daron Acemoglu has long studied technology-driven growth. Here’s how he’s thinking about AI’s effect on the economy.

Researchers have developed a web plug-in to help those looking to protect their mental health make more informed decisions.

MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.

First organized MIT delegation highlights the Institute's growing commitment to addressing climate change by showcasing research on biodiversity conservation, AI, and the role of local communities.

Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.

This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.

Associate Professor Catherine D’Ignazio thinks carefully about how we acquire and display data — and why we lack it for many things.

Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.

The method could help communities visualize and prepare for approaching storms.

The MIT Advanced Vehicle Technology Consortium provides data-driven insights into driver behavior, along with trust in AI and advanced vehicle technology.

In a talk at MIT, White House science advisor Arati Prabhakar outlined challenges in medicine, climate, and AI, while expressing resolve to tackle hard problems.

The technique could make AI systems better at complex tasks that involve variability.

The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.

Acclaimed keyboardist Jordan Rudess’s collaboration with the MIT Media Lab culminates in live improvisation between an AI “jam_bot” and the artist.

MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI's potential for creating robotics training data.

Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.

Progress on the energy transition depends on collective action benefiting all stakeholders, agreed participants in MITEI’s annual research conference.

An AI method developed by Professor Markus Buehler finds hidden links between science and art to suggest novel materials.

MIT and IBM researchers are creating linkage mechanisms to innovate human-AI kinematic engineering.

By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.

A new design tool uses UV and RGB lights to change the color and textures of everyday objects. The system could enable surfaces to display dynamic patterns, such as health data and fashion designs.

Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.

Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.

Exploring biodiversity, linguistic diversity, and collective AI-generated poetry, her work will be honored with a $100K prize, artist residency, and public lecture at MIT in spring 2025.

Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.

The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.

A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.

Alumni-founded Ambience Healthcare automates routine tasks for clinicians before, during, and after patient visits.

Collaborative multi-university team will pursue new AI-enhanced design tools and high-throughput testing methods for next-generation turbomachinery.

Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.

MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.

David Singer, head of the MIT Department of Political Science, discusses the Strengthening Democracy Initiative, focused on the rigorous study of elections, public opinion, and political participation.

By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.

New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.

A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.

The program will invite students to investigate new vistas at the intersection of music, computing, and technology.

The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.

The innovations map the ocean floor and the brain, prevent heat stroke and cognitive injury, expand AI processing and quantum system capabilities, and introduce new fabrication approaches.

Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.

MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times.

The digital adviser helps users swiftly navigate the 24-step “Disciplined Entrepreneurship” process.

By analyzing X-ray crystallography data, the model could help researchers develop new materials for many applications, including batteries and magnets.

Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.

“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.

“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.